A drive assist system for assisting a driver to drive a vehicle is typically designed to acquire traffic information relating to crossroads, momentary stop positions, curves, a vehicle approaching from ahead, and other information requiring the driver to decelerate the vehicle, by means of a vehicle-mounted camera, a navigation system, or the like. The drive assist system then provides drive assist to the driver based on the traffic information around the vehicle thus acquired, for example by giving the driver a voice guidance message to decelerate.
This type of drive assist is generally performed, using a standard travel pattern obtained by averaging data of various driving behaviors including perception of traffic information, judgment, and driving operation of ordinary drivers, which are measured under a predetermined travel model such as a mock driving course. PTL 1, for example, describes a system that firstly generates exemplary operation data indicating time-series transition of exemplary operation amount of operating equipment such as an accelerator pedal or a brake pedal based on information relating to an approach speed for a crossroads or a curve, and the shape of a road such as curvature radius of a crossroads or a curve (travel model). The system then registers the generated exemplary operation data in a database as an exemplary driving model (standard travel pattern). The system presents, to a driver of a vehicle to be assisted, the exemplary operation data registered in the database simultaneously with data on transition in operation amount of various operating equipment by the driver, so that the driving behavior of the driver is evaluated.
A travel pattern of a vehicle approaching a crossroads or a momentary stop position generally varies according to road environment having a variety of elements such as curvature of a road curve, width and inclination of a road, as well as according to the driver's driving habit or driving technique. It is difficult to adapt standardized travel patterns to such a varying travel pattern of a driver. This means that it is not realistic to generate an exemplary driving model based on the actual road environment, driver's driving habit and driving technique, since it takes a huge amount of man-hours to generate such a model.
On the other hand, another method has recently been studied, wherein a vehicle data group collected during driving operation by a plurality of drivers is stratified according to their driving techniques, that is, their driving performance levels, by analyzing vehicle data indicating transition of driving operation by the drivers. However, even with such a method of stratifying the vehicle data group according to drivers' driving performance levels, it is still difficult to identify what kind of a driving operation causes a difference in driving performance level among the drivers. Therefore, it has not been successful to identify the factors for which different drivers have different driving performance levels. In other words, it has not been successful to identify the driving elements to be assisted in order to improve the driving performance level. This means that characteristics of vehicle data collected based on drivers' driving operations cannot be quantitatively understood.